Lecturer: Huang Kun, professor ofIndiana University.

Date and time: 26th August, 8:30.

Place: ZOOM ID: 210 089 8623 Password: 123456

Organizer: School of Mathematics and Statistics

Warmly Welcome!

Abstract of report:With the advancement in high throughput technologies, various types of "omics" data have been generated for human disease studies. In particular, large amounts of "multi-omics" data have been generated for important diseases such as cancer and Alzheimer's disease. However, computationally it is challenging to integrate the multiple types of omics data given the high dimensionality, heterogeneous data type, high dynamical range, and multiple tasks associated with the integration. In this talk, I will introduce our work over the past decade on applying different machine learning approaches for integrative analysis of multi-omics data. These methods were driven by translational applications such as cancers and Alzheimer's diseases. These methods have led to new biological hypotheses related to these diseases.

Huang Kun obtained his twobachelordegrees in Biological Sciences and ElectronicComputerfromTsinghua Universityin 1996, and acquired his three master degrees in Physiology, Electronic Engineering and then Mathematics from AmericanUniversity of Illinois at Urbana-Champaign, later in 2004 he gained his doctorate inElectrical andComputerEngineering.

In 2010, he was honored witha tenure-track position in The Ohio State University.In 2017, he joined in aPrecision HealthProgram participated and led by School of Medicine ofIndiana University, and served as the director of Data Science and Informaticsand meanwhile appointed as a professor of Genome Data Science. He also worked as the associatedean for Data Science in the School of Medicineand deputy director of Simon Cancer Center inIndiana University.In 2018, he was elected fellow of American Institute for Medical and Biological Engineering. His major researchfields are Bioinformatics,MedicalImageAnalysis, Medical Big Data, the Application of Machine Learning in the Study on Cancer and Neuroscience. He has published over 200 research papers altogether.